Data Strategy

A data strategy ensures that data is viewed as a strategic resource and is used systematically.

Data Strategy defines how an organization collects, stores, manages, analyzes, and uses data and how the use of data affects business goals. Without an appropriate data strategy, companies risk not using data effectively and therefore unable to derive valuable insights.

Why is a data strategy important?

Data is the raw material of the 21st century. Companies that use their data effectively can:

Make well-founded decisions: Data-based decisions are more precise, efficient and lead to better results.
Optimize processes: By automating and optimizing data-based processes, companies can save time, resources and costs.
Understanding customers better: By means data analysis Companies gain valuable insights into customer needs, preferences and behaviours, which can be used to develop targeted products and services.
Develop new business models: Data makes it possible to identify new market opportunities and develop innovative business models.
Ensuring a competitive advantage: Companies with a effective data strategy have a clear advantage over their data-uninformed competitors.

Elements of a comprehensive data strategy

A successful data strategy comprises the following core components:

Vision and mission

Vision: What is the long-term goal of data use in companies?
Mission: What specific steps are being taken to achieve the vision?

Objectives and indicators

Objectives: What measurable and time-bound results should be achieved with the data strategy?
Key figures: Which metrics and indicators are used to measure the success of the data strategy?

Initiatives and action plan

Initiatives: What specific projects and activities are being carried out to achieve the goals?
Action plan: How are the initiatives implemented in terms of time and content?

Governance and Responsibilities

Governance: Who is responsible for implementing and maintaining the data strategy?
Roles and responsibilities: Who takes on which tasks and decisions as part of the data strategy?

Data management and infrastructure

data sources: Where does the relevant data for corporate goals come from?
Data storage: How is data stored securely and efficiently?
data quality: How is the quality and consistency of the data ensured?
Data security: How is data protected against unauthorized access, loss, or damage?

Data technologies and tools

What technologies and tools are needed to implement the data strategy?
How is the data integrates, analyses and visualizes?
How is access to data and analytics made available to authorized users?

Data talent and qualification

What skills and competencies do employees need to implement the data strategy?
How do employees in the area Data literacy and Data Governance trained?
How is it ensured that new knowledge and new skills are continuously built up in the company?

Performance measurement and reporting

Which metrics and indicators are used to measure the success of the data strategy?
How are the results regularly evaluated and reported?
What adjustments and optimizations are required based on the results?

Creating and Implementing a Data Strategy: An Iterative Process

The development and implementation of a data strategy is an iterative process that is continuously adapted and developed. Here you can find more information about our approach to Data Strategy.

In our opinion, typical steps include:

1. Current situation analysis

What data does the company currently generate and use?

Internal data sources (e.g. CRM systems, ERP systems, transaction data)
Externe data sources (e.g. market data, social media data, customer data from third parties)
Unstructured data (e.g. emails, documents, images, videos)
structured data (e.g. customer data, transaction data, sensor data, financial data)

How is the data managed, analyzed and used?

Data storage solutions (e.g. data warehouses, Data Lakes)
data analysistools (e.g. Business intelligence (BI) tools, data mining-Software)
Data visualization tools (e.g. dashboards, reports)

What are the challenges and opportunities with regard to data usage and management?

data quality and consistency
Data security and privacy
Data integration and availability
Shortage of skilled workers in the area Data Science and analytics
Ethical aspects of data use

We are happy to assist with the analysis as part of a Data Audit or data advice.

2. Objective

What business goals should be achieved with the Data Strategy be achieved?

Increasing efficiency and productivity
Improving decision making
Development of new products and services
Improving customer experience
Development of new business models

What role should data play in achieving these goals?

What types of data analyses are required?
What insights should be gained from the data?
How should the knowledge gained be translated into business decisions?

What specific results should be achieved?

Quantifiable goals with clear timelines
Success indicators to measure progress

3. Strategy development

What initiatives and measures are needed to achieve the goals?

Development of use cases for data usage
implementation of data management-Practices
Introduction of Data governance policies
Building a data analytics team
Procurement of technologies and tools

What technologies, tools, and resources are needed?

data warehouse- and Data Lake-Solutions
data mining- and analysis software
Data visualization tools
cloud computing platforms
Training and continuing education for employees
Feel free to take a look at our services Data Culture and Data Consulting Gone

How to become Governance, defined responsibilities and processes?

Who is responsible for implementing the data strategy responsible?
What are the roles and responsibilities in the area of data usage and management?
Which processes and standards are defined for data processing?
We like to define this together, for example in Data Governance Framework

4. Implementation and implementation

Prioritize and schedule initiatives

Preparation of a detailed project plan with milestones and responsibilities
Allocation of resources and budget
Communication of data strategy to all employees

Implementation of individual initiatives

Selecting and implementing technologies and tools
Train and engage employees
Monitoring and optimization of processes

Continuous improvement and adjustment

Regular review of progress and goal achievement
Adapting the strategy to changing business requirements and technological developments
Learn from experiences and best practices
That works best with a coordinated data organization.

Let us know in detail about Data Strategy and talk about the goals, challenges and next steps.

Success factors for implementing a data strategy

Enterprise-wide support: Die data strategy must be supported by management and all departments of the company.
Clear communication: The goals, initiatives, and results of the data strategy must be clearly and transparently communicated.
Involving employees: Employees at all levels should participate in the development and implementation of data strategy be included.
Data literacy: The company must have the necessary data skills to Data Strategy Implement successfully.
Agility and adaptability: Die Data strategy must be flexible And can be adapted to changing business requirements and technological developments.
Continuous improvement: Die Data strategy must be continuous Be monitored, evaluated and optimized.